Resampling methods for tests in regression models with autocorrelated errors

Abstract This paper presents the results of a Monte Carlo study which suggest that the bootstrap — in combination with a bias-reduction method such as the half-sample jackknife — substantially corrects the problem in small and moderate samples of excessive Type I error probabilities in tests on the coefficients in regression models with serially correlated disturbances. The methods are likely to be applicable to testing in many other regression situations.

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